Overview

Dataset statistics

Number of variables38
Number of observations20343
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 MiB
Average record size in memory304.0 B

Variable types

Numeric11
Text2
Categorical25

Alerts

ID is highly overall correlated with Year PublishedHigh correlation
Year Published is highly overall correlated with IDHigh correlation
Play Time is highly overall correlated with Complexity AverageHigh correlation
Min Age is highly overall correlated with ChildrenHigh correlation
Users Rated is highly overall correlated with BGG Rank and 1 other fieldsHigh correlation
Rating Average is highly overall correlated with BGG Rank and 1 other fieldsHigh correlation
BGG Rank is highly overall correlated with Users Rated and 2 other fieldsHigh correlation
Complexity Average is highly overall correlated with Play Time and 1 other fieldsHigh correlation
Owned Users is highly overall correlated with Users Rated and 1 other fieldsHigh correlation
Domains is highly overall correlated with Mech_board and 9 other fieldsHigh correlation
Mech_board is highly overall correlated with DomainsHigh correlation
Abstract is highly overall correlated with DomainsHigh correlation
Children is highly overall correlated with Min Age and 1 other fieldsHigh correlation
Customizable is highly overall correlated with DomainsHigh correlation
Family is highly overall correlated with DomainsHigh correlation
Party is highly overall correlated with DomainsHigh correlation
Strategy is highly overall correlated with DomainsHigh correlation
Thematic is highly overall correlated with DomainsHigh correlation
Wargames is highly overall correlated with DomainsHigh correlation
Domain_Not Defined is highly overall correlated with DomainsHigh correlation
Mech Not Defined is highly imbalanced (60.3%)Imbalance
Mech_Acting is highly imbalanced (60.0%)Imbalance
Mech_tokens is highly imbalanced (98.4%)Imbalance
Mech_construcc_farm is highly imbalanced (55.4%)Imbalance
Mech_score is highly imbalanced (76.7%)Imbalance
Mech_turnbased is highly imbalanced (66.4%)Imbalance
Mech_skill is highly imbalanced (66.4%)Imbalance
Mech_solo is highly imbalanced (78.3%)Imbalance
Abstract is highly imbalanced (70.3%)Imbalance
Children is highly imbalanced (75.0%)Imbalance
Customizable is highly imbalanced (89.0%)Imbalance
Family is highly imbalanced (51.0%)Imbalance
Party is highly imbalanced (80.7%)Imbalance
Strategy is highly imbalanced (50.5%)Imbalance
Thematic is highly imbalanced (68.2%)Imbalance
Year Published is highly skewed (γ1 = -24.05339023)Skewed
Max Players is highly skewed (γ1 = 43.49841135)Skewed
Play Time is highly skewed (γ1 = 73.63777821)Skewed
BGG Rank is uniformly distributedUniform
BGG Rank has unique valuesUnique
Play Time has 556 (2.7%) zerosZeros
Min Age has 1251 (6.1%) zerosZeros
Complexity Average has 426 (2.1%) zerosZeros

Reproduction

Analysis started2023-06-18 12:11:40.805714
Analysis finished2023-06-18 12:11:56.115989
Duration15.31 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Distinct20327
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108239.27
Minimum1
Maximum331787
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-18T14:11:56.206511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1365.2
Q111029
median88957
Q3192965.5
95-th percentile277409
Maximum331787
Range331786
Interquartile range (IQR)181936.5

Descriptive statistics

Standard deviation98693.219
Coefficient of variation (CV)0.91180603
Kurtosis-1.2737249
Mean108239.27
Median Absolute Deviation (MAD)82285
Skewness0.41615653
Sum2.2019115 × 109
Variance9.7403515 × 109
MonotonicityNot monotonic
2023-06-18T14:11:56.292030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9360 2
 
< 0.1%
41788 2
 
< 0.1%
254157 2
 
< 0.1%
28301 2
 
< 0.1%
1003 2
 
< 0.1%
97093 2
 
< 0.1%
202077 2
 
< 0.1%
8393 2
 
< 0.1%
299171 2
 
< 0.1%
183401 2
 
< 0.1%
Other values (20317) 20323
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
331787 1
< 0.1%
329465 1
< 0.1%
328871 1
< 0.1%
326624 1
< 0.1%
326485 1
< 0.1%
325635 1
< 0.1%
325555 1
< 0.1%
325494 1
< 0.1%
325022 1
< 0.1%
324345 1
< 0.1%

Name
Text

Distinct19976
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
2023-06-18T14:11:56.463063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length107
Median length85
Mean length18.434253
Min length1

Characters and Unicode

Total characters375008
Distinct characters268
Distinct categories19 ?
Distinct scripts8 ?
Distinct blocks14 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19653 ?
Unique (%)96.6%

Sample

1st rowGloomhaven
2nd rowPandemic Legacy: Season 1
3rd rowBrass: Birmingham
4th rowTerraforming Mars
5th rowTwilight Imperium: Fourth Edition
ValueCountFrequency (%)
the 3987
 
6.5%
of 2178
 
3.6%
game 1372
 
2.2%
885
 
1.4%
war 546
 
0.9%
edition 505
 
0.8%
in 475
 
0.8%
a 376
 
0.6%
card 369
 
0.6%
battle 341
 
0.6%
Other values (15953) 50156
82.0%
2023-06-18T14:11:56.732579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40896
 
10.9%
e 33256
 
8.9%
a 27177
 
7.2%
o 21800
 
5.8%
r 21129
 
5.6%
i 20184
 
5.4%
n 18869
 
5.0%
t 18489
 
4.9%
s 15457
 
4.1%
l 13043
 
3.5%
Other values (258) 144708
38.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 261681
69.8%
Uppercase Letter 55344
 
14.8%
Space Separator 40897
 
10.9%
Other Punctuation 8801
 
2.3%
Decimal Number 6406
 
1.7%
Dash Punctuation 1262
 
0.3%
Open Punctuation 238
 
0.1%
Close Punctuation 238
 
0.1%
Other Letter 100
 
< 0.1%
Math Symbol 12
 
< 0.1%
Other values (9) 29
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 33256
12.7%
a 27177
10.4%
o 21800
 
8.3%
r 21129
 
8.1%
i 20184
 
7.7%
n 18869
 
7.2%
t 18489
 
7.1%
s 15457
 
5.9%
l 13043
 
5.0%
h 9481
 
3.6%
Other values (82) 62796
24.0%
Other Letter
ValueCountFrequency (%)
6
 
6.0%
5
 
5.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
2
 
2.0%
2
 
2.0%
2
 
2.0%
Other values (58) 68
68.0%
Uppercase Letter
ValueCountFrequency (%)
T 5523
 
10.0%
S 4890
 
8.8%
C 4512
 
8.2%
B 3463
 
6.3%
G 3365
 
6.1%
D 3357
 
6.1%
M 3228
 
5.8%
A 3211
 
5.8%
W 2572
 
4.6%
P 2561
 
4.6%
Other values (43) 18662
33.7%
Other Punctuation
ValueCountFrequency (%)
: 4776
54.3%
! 1114
 
12.7%
' 1001
 
11.4%
, 573
 
6.5%
. 528
 
6.0%
& 502
 
5.7%
? 154
 
1.7%
/ 44
 
0.5%
\ 28
 
0.3%
" 26
 
0.3%
Other values (9) 55
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 1695
26.5%
9 752
11.7%
4 743
11.6%
0 738
11.5%
2 549
 
8.6%
8 521
 
8.1%
5 429
 
6.7%
3 354
 
5.5%
6 319
 
5.0%
7 306
 
4.8%
Other Number
ValueCountFrequency (%)
2
40.0%
³ 1
20.0%
1
20.0%
² 1
20.0%
Math Symbol
ValueCountFrequency (%)
+ 10
83.3%
× 1
 
8.3%
1
 
8.3%
Currency Symbol
ValueCountFrequency (%)
$ 9
75.0%
2
 
16.7%
¥ 1
 
8.3%
Other Symbol
ValueCountFrequency (%)
1
33.3%
1
33.3%
° 1
33.3%
Space Separator
ValueCountFrequency (%)
40896
> 99.9%
  1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1261
99.9%
1
 
0.1%
Nonspacing Mark
ValueCountFrequency (%)
́ 1
50.0%
1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 238
100.0%
Close Punctuation
ValueCountFrequency (%)
) 238
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%
Modifier Letter
ValueCountFrequency (%)
1
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 316972
84.5%
Common 57881
 
15.4%
Katakana 54
 
< 0.1%
Cyrillic 27
 
< 0.1%
Greek 26
 
< 0.1%
Han 25
 
< 0.1%
Hiragana 21
 
< 0.1%
Inherited 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 33256
 
10.5%
a 27177
 
8.6%
o 21800
 
6.9%
r 21129
 
6.7%
i 20184
 
6.4%
n 18869
 
6.0%
t 18489
 
5.8%
s 15457
 
4.9%
l 13043
 
4.1%
h 9481
 
3.0%
Other values (104) 118087
37.3%
Common
ValueCountFrequency (%)
40896
70.7%
: 4776
 
8.3%
1 1695
 
2.9%
- 1261
 
2.2%
! 1114
 
1.9%
' 1001
 
1.7%
9 752
 
1.3%
4 743
 
1.3%
0 738
 
1.3%
, 573
 
1.0%
Other values (43) 4332
 
7.5%
Katakana
ValueCountFrequency (%)
6
 
11.1%
5
 
9.3%
3
 
5.6%
3
 
5.6%
3
 
5.6%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
Other values (21) 24
44.4%
Han
ValueCountFrequency (%)
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
Other values (15) 15
60.0%
Greek
ValueCountFrequency (%)
α 3
11.5%
ο 3
11.5%
λ 2
 
7.7%
μ 2
 
7.7%
ά 2
 
7.7%
ρ 2
 
7.7%
ε 2
 
7.7%
Ξ 1
 
3.8%
θ 1
 
3.8%
ι 1
 
3.8%
Other values (7) 7
26.9%
Cyrillic
ValueCountFrequency (%)
т 4
14.8%
и 4
14.8%
р 2
7.4%
у 2
7.4%
а 2
7.4%
С 2
7.4%
в 2
7.4%
к 2
7.4%
Э 2
7.4%
п 1
 
3.7%
Other values (4) 4
14.8%
Hiragana
ValueCountFrequency (%)
3
14.3%
3
14.3%
2
9.5%
2
9.5%
2
9.5%
2
9.5%
2
9.5%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (2) 2
9.5%
Inherited
ValueCountFrequency (%)
́ 1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 374120
99.8%
None 744
 
0.2%
Katakana 55
 
< 0.1%
Cyrillic 27
 
< 0.1%
CJK 25
 
< 0.1%
Hiragana 21
 
< 0.1%
Punctuation 8
 
< 0.1%
Currency Symbols 2
 
< 0.1%
Diacriticals 1
 
< 0.1%
VS 1
 
< 0.1%
Other values (4) 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40896
 
10.9%
e 33256
 
8.9%
a 27177
 
7.3%
o 21800
 
5.8%
r 21129
 
5.6%
i 20184
 
5.4%
n 18869
 
5.0%
t 18489
 
4.9%
s 15457
 
4.1%
l 13043
 
3.5%
Other values (73) 143820
38.4%
None
ValueCountFrequency (%)
é 112
15.1%
ä 88
 
11.8%
ü 86
 
11.6%
ö 68
 
9.1%
ó 42
 
5.6%
á 35
 
4.7%
í 22
 
3.0%
ł 20
 
2.7%
ß 19
 
2.6%
à 19
 
2.6%
Other values (80) 233
31.3%
Katakana
ValueCountFrequency (%)
6
 
10.9%
5
 
9.1%
3
 
5.5%
3
 
5.5%
3
 
5.5%
2
 
3.6%
2
 
3.6%
2
 
3.6%
2
 
3.6%
2
 
3.6%
Other values (22) 25
45.5%
Punctuation
ValueCountFrequency (%)
4
50.0%
1
 
12.5%
1
 
12.5%
1
 
12.5%
1
 
12.5%
Cyrillic
ValueCountFrequency (%)
т 4
14.8%
и 4
14.8%
р 2
7.4%
у 2
7.4%
а 2
7.4%
С 2
7.4%
в 2
7.4%
к 2
7.4%
Э 2
7.4%
п 1
 
3.7%
Other values (4) 4
14.8%
Hiragana
ValueCountFrequency (%)
3
14.3%
3
14.3%
2
9.5%
2
9.5%
2
9.5%
2
9.5%
2
9.5%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (2) 2
9.5%
Currency Symbols
ValueCountFrequency (%)
2
100.0%
CJK
ValueCountFrequency (%)
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
Other values (15) 15
60.0%
Diacriticals
ValueCountFrequency (%)
́ 1
100.0%
VS
ValueCountFrequency (%)
1
100.0%
Misc Symbols
ValueCountFrequency (%)
1
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%
Math Operators
ValueCountFrequency (%)
1
100.0%
Latin Ext Additional
ValueCountFrequency (%)
1
100.0%

Year Published
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct179
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.2062
Minimum400
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-18T14:11:56.826087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile1978
Q12001
median2011
Q32016
95-th percentile2019
Maximum2022
Range1622
Interquartile range (IQR)15

Descriptive statistics

Standard deviation40.87024
Coefficient of variation (CV)0.020392233
Kurtosis759.45976
Mean2004.2062
Median Absolute Deviation (MAD)6
Skewness-24.05339
Sum40771567
Variance1670.3766
MonotonicityNot monotonic
2023-06-18T14:11:56.910602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2017 1274
 
6.3%
2016 1257
 
6.2%
2018 1254
 
6.2%
2019 1134
 
5.6%
2015 1131
 
5.6%
2014 987
 
4.9%
2013 850
 
4.2%
2012 815
 
4.0%
2011 735
 
3.6%
2010 692
 
3.4%
Other values (169) 10214
50.2%
ValueCountFrequency (%)
400 2
< 0.1%
500 1
< 0.1%
550 1
< 0.1%
600 1
< 0.1%
650 1
< 0.1%
700 2
< 0.1%
762 1
< 0.1%
1000 2
< 0.1%
1125 1
< 0.1%
1150 1
< 0.1%
ValueCountFrequency (%)
2022 1
 
< 0.1%
2021 144
 
0.7%
2020 684
3.4%
2019 1134
5.6%
2018 1254
6.2%
2017 1274
6.3%
2016 1257
6.2%
2015 1131
5.6%
2014 987
4.9%
2013 850
4.2%

Min Players
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0197119
Minimum0
Maximum10
Zeros46
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-18T14:11:56.987116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69036572
Coefficient of variation (CV)0.34181395
Kurtosis10.937481
Mean2.0197119
Median Absolute Deviation (MAD)0
Skewness1.7359
Sum41087
Variance0.47660483
MonotonicityNot monotonic
2023-06-18T14:11:57.046117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 14076
69.2%
1 3270
 
16.1%
3 2365
 
11.6%
4 474
 
2.3%
5 57
 
0.3%
0 46
 
0.2%
6 21
 
0.1%
8 17
 
0.1%
7 14
 
0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
0 46
 
0.2%
1 3270
 
16.1%
2 14076
69.2%
3 2365
 
11.6%
4 474
 
2.3%
5 57
 
0.3%
6 21
 
0.1%
7 14
 
0.1%
8 17
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 1
 
< 0.1%
8 17
 
0.1%
7 14
 
0.1%
6 21
 
0.1%
5 57
 
0.3%
4 474
 
2.3%
3 2365
 
11.6%
2 14076
69.2%
1 3270
 
16.1%

Max Players
Real number (ℝ)

Distinct54
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6722214
Minimum0
Maximum999
Zeros161
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-18T14:11:57.121631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median4
Q36
95-th percentile10
Maximum999
Range999
Interquartile range (IQR)2

Descriptive statistics

Standard deviation15.231376
Coefficient of variation (CV)2.6852576
Kurtosis2693.0695
Mean5.6722214
Median Absolute Deviation (MAD)2
Skewness43.498411
Sum115390
Variance231.99481
MonotonicityNot monotonic
2023-06-18T14:11:57.233374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 6376
31.3%
2 4074
20.0%
6 3723
18.3%
5 2814
13.8%
8 1154
 
5.7%
10 380
 
1.9%
1 313
 
1.5%
7 309
 
1.5%
3 272
 
1.3%
12 236
 
1.2%
Other values (44) 692
 
3.4%
ValueCountFrequency (%)
0 161
 
0.8%
1 313
 
1.5%
2 4074
20.0%
3 272
 
1.3%
4 6376
31.3%
5 2814
13.8%
6 3723
18.3%
7 309
 
1.5%
8 1154
 
5.7%
9 73
 
0.4%
ValueCountFrequency (%)
999 3
 
< 0.1%
362 1
 
< 0.1%
200 1
 
< 0.1%
163 1
 
< 0.1%
127 1
 
< 0.1%
120 1
 
< 0.1%
100 14
 
0.1%
99 136
0.7%
75 2
 
< 0.1%
69 1
 
< 0.1%

Play Time
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct116
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.294548
Minimum0
Maximum60000
Zeros556
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-18T14:11:57.324891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q130
median45
Q390
95-th percentile240
Maximum60000
Range60000
Interquartile range (IQR)60

Descriptive statistics

Standard deviation545.4472
Coefficient of variation (CV)5.9745868
Kurtosis7406.6538
Mean91.294548
Median Absolute Deviation (MAD)25
Skewness73.637778
Sum1857205
Variance297512.65
MonotonicityNot monotonic
2023-06-18T14:11:57.404408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 3638
17.9%
60 3003
14.8%
45 2107
10.4%
20 2026
10.0%
120 1618
8.0%
90 1591
7.8%
15 1230
 
6.0%
180 805
 
4.0%
10 758
 
3.7%
0 556
 
2.7%
Other values (106) 3011
14.8%
ValueCountFrequency (%)
0 556
2.7%
1 23
 
0.1%
2 11
 
0.1%
3 3
 
< 0.1%
4 3
 
< 0.1%
5 138
 
0.7%
6 5
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
10 758
3.7%
ValueCountFrequency (%)
60000 1
 
< 0.1%
22500 1
 
< 0.1%
17280 1
 
< 0.1%
14400 1
 
< 0.1%
12000 2
 
< 0.1%
10000 1
 
< 0.1%
8640 1
 
< 0.1%
7920 1
 
< 0.1%
6000 8
< 0.1%
5400 1
 
< 0.1%

Min Age
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6014845
Minimum0
Maximum25
Zeros1251
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-18T14:11:57.477925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median10
Q312
95-th percentile14
Maximum25
Range25
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.6454579
Coefficient of variation (CV)0.37967649
Kurtosis0.96300028
Mean9.6014845
Median Absolute Deviation (MAD)2
Skewness-0.85045773
Sum195323
Variance13.289364
MonotonicityNot monotonic
2023-06-18T14:11:57.543926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
12 4704
23.1%
8 4065
20.0%
10 3870
19.0%
14 1780
 
8.7%
0 1251
 
6.1%
13 1133
 
5.6%
6 917
 
4.5%
7 809
 
4.0%
5 436
 
2.1%
9 326
 
1.6%
Other values (11) 1052
 
5.2%
ValueCountFrequency (%)
0 1251
 
6.1%
1 2
 
< 0.1%
2 15
 
0.1%
3 114
 
0.6%
4 268
 
1.3%
5 436
 
2.1%
6 917
 
4.5%
7 809
 
4.0%
8 4065
20.0%
9 326
 
1.6%
ValueCountFrequency (%)
25 1
 
< 0.1%
21 11
 
0.1%
18 171
 
0.8%
17 59
 
0.3%
16 167
 
0.8%
15 147
 
0.7%
14 1780
 
8.7%
13 1133
 
5.6%
12 4704
23.1%
11 97
 
0.5%

Users Rated
Real number (ℝ)

Distinct2973
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean840.97139
Minimum30
Maximum102214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-18T14:11:57.621445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q155
median120
Q3385
95-th percentile3158.9
Maximum102214
Range102184
Interquartile range (IQR)330

Descriptive statistics

Standard deviation3511.5622
Coefficient of variation (CV)4.1756025
Kurtosis223.41521
Mean840.97139
Median Absolute Deviation (MAD)80
Skewness12.353199
Sum17107881
Variance12331069
MonotonicityNot monotonic
2023-06-18T14:11:57.714966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 271
 
1.3%
33 263
 
1.3%
31 262
 
1.3%
35 250
 
1.2%
32 248
 
1.2%
34 243
 
1.2%
39 239
 
1.2%
38 220
 
1.1%
41 217
 
1.1%
44 211
 
1.0%
Other values (2963) 17919
88.1%
ValueCountFrequency (%)
30 271
1.3%
31 262
1.3%
32 248
1.2%
33 263
1.3%
34 243
1.2%
35 250
1.2%
36 210
1.0%
37 207
1.0%
38 220
1.1%
39 239
1.2%
ValueCountFrequency (%)
102214 1
< 0.1%
101853 1
< 0.1%
101510 1
< 0.1%
84371 1
< 0.1%
78089 1
< 0.1%
71611 1
< 0.1%
67688 1
< 0.1%
64864 1
< 0.1%
63498 1
< 0.1%
63128 1
< 0.1%

Rating Average
Real number (ℝ)

Distinct622
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4032267
Minimum1.05
Maximum9.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-18T14:11:57.804478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.05
5-th percentile4.82
Q15.82
median6.43
Q37.03
95-th percentile7.88
Maximum9.58
Range8.53
Interquartile range (IQR)1.21

Descriptive statistics

Standard deviation0.93591053
Coefficient of variation (CV)0.14616233
Kurtosis0.70059269
Mean6.4032267
Median Absolute Deviation (MAD)0.6
Skewness-0.29971779
Sum130260.84
Variance0.87592851
MonotonicityNot monotonic
2023-06-18T14:11:57.884993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5 110
 
0.5%
6.38 109
 
0.5%
6.43 107
 
0.5%
6.67 104
 
0.5%
6.72 104
 
0.5%
6.27 103
 
0.5%
6.28 103
 
0.5%
6.51 103
 
0.5%
6.53 102
 
0.5%
6.17 102
 
0.5%
Other values (612) 19296
94.9%
ValueCountFrequency (%)
1.05 1
< 0.1%
1.1 1
< 0.1%
1.32 1
< 0.1%
1.43 1
< 0.1%
1.5 1
< 0.1%
1.54 1
< 0.1%
1.55 1
< 0.1%
1.78 1
< 0.1%
1.9 1
< 0.1%
2.06 1
< 0.1%
ValueCountFrequency (%)
9.58 1
< 0.1%
9.54 1
< 0.1%
9.46 1
< 0.1%
9.43 2
< 0.1%
9.34 1
< 0.1%
9.31 1
< 0.1%
9.25 1
< 0.1%
9.24 2
< 0.1%
9.23 2
< 0.1%
9.22 1
< 0.1%

BGG Rank
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct20343
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10172.89
Minimum1
Maximum20344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-18T14:11:57.968511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1018.1
Q15087.5
median10173
Q315258.5
95-th percentile19326.9
Maximum20344
Range20343
Interquartile range (IQR)10171

Descriptive statistics

Standard deviation5872.8316
Coefficient of variation (CV)0.57730216
Kurtosis-1.1999292
Mean10172.89
Median Absolute Deviation (MAD)5086
Skewness-7.7848833 × 10-5
Sum2.0694711 × 108
Variance34490151
MonotonicityStrictly increasing
2023-06-18T14:11:58.054518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
13562 1
 
< 0.1%
13569 1
 
< 0.1%
13568 1
 
< 0.1%
13567 1
 
< 0.1%
13566 1
 
< 0.1%
13565 1
 
< 0.1%
13564 1
 
< 0.1%
13563 1
 
< 0.1%
13561 1
 
< 0.1%
Other values (20333) 20333
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
20344 1
< 0.1%
20343 1
< 0.1%
20342 1
< 0.1%
20341 1
< 0.1%
20340 1
< 0.1%
20339 1
< 0.1%
20338 1
< 0.1%
20337 1
< 0.1%
20336 1
< 0.1%
20335 1
< 0.1%

Complexity Average
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct379
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9911876
Minimum0
Maximum5
Zeros426
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-18T14:11:58.142026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.33
median1.97
Q32.54
95-th percentile3.529
Maximum5
Range5
Interquartile range (IQR)1.21

Descriptive statistics

Standard deviation0.84890322
Coefficient of variation (CV)0.4263301
Kurtosis0.012871143
Mean1.9911876
Median Absolute Deviation (MAD)0.63
Skewness0.41369357
Sum40506.73
Variance0.72063668
MonotonicityNot monotonic
2023-06-18T14:11:58.224539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2306
 
11.3%
2 1563
 
7.7%
1.5 692
 
3.4%
3 498
 
2.4%
1.33 448
 
2.2%
0 426
 
2.1%
1.67 407
 
2.0%
2.5 394
 
1.9%
2.33 296
 
1.5%
1.25 292
 
1.4%
Other values (369) 13021
64.0%
ValueCountFrequency (%)
0 426
 
2.1%
1 2306
11.3%
1.01 1
 
< 0.1%
1.02 12
 
0.1%
1.03 22
 
0.1%
1.04 28
 
0.1%
1.05 53
 
0.3%
1.06 55
 
0.3%
1.07 48
 
0.2%
1.08 66
 
0.3%
ValueCountFrequency (%)
5 1
< 0.1%
4.93 1
< 0.1%
4.91 2
< 0.1%
4.9 1
< 0.1%
4.89 2
< 0.1%
4.86 1
< 0.1%
4.84 1
< 0.1%
4.8 1
< 0.1%
4.78 1
< 0.1%
4.77 1
< 0.1%

Owned Users
Real number (ℝ)

Distinct3997
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1408.4571
Minimum0
Maximum155312
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-18T14:11:58.307053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile65
Q1146
median310
Q3867
95-th percentile5239
Maximum155312
Range155312
Interquartile range (IQR)721

Descriptive statistics

Standard deviation5037.3291
Coefficient of variation (CV)3.5764874
Kurtosis231.2697
Mean1408.4571
Median Absolute Deviation (MAD)209
Skewness12.319322
Sum28652243
Variance25374685
MonotonicityNot monotonic
2023-06-18T14:11:58.391567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122 67
 
0.3%
80 66
 
0.3%
109 63
 
0.3%
105 63
 
0.3%
73 63
 
0.3%
106 63
 
0.3%
103 62
 
0.3%
119 61
 
0.3%
100 60
 
0.3%
104 59
 
0.3%
Other values (3987) 19716
96.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
3 4
< 0.1%
4 1
 
< 0.1%
5 3
< 0.1%
6 7
< 0.1%
7 2
 
< 0.1%
9 2
 
< 0.1%
10 6
< 0.1%
11 2
 
< 0.1%
12 7
< 0.1%
ValueCountFrequency (%)
155312 1
< 0.1%
154531 1
< 0.1%
149337 1
< 0.1%
112410 1
< 0.1%
107682 1
< 0.1%
101839 1
< 0.1%
97463 1
< 0.1%
94343 1
< 0.1%
92896 1
< 0.1%
87099 1
< 0.1%
Distinct7382
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
2023-06-18T14:11:58.575598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length406
Median length247
Mean length47.968097
Min length4

Characters and Unicode

Total characters975815
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5866 ?
Unique (%)28.8%

Sample

1st rowAction Queue, Action Retrieval, Campaign / Battle Card Driven, Card Play Conflict Resolution, Communication Limits, Cooperative Game, Deck Construction, Deck Bag and Pool Building, Grid Movement, Hand Management, Hexagon Grid, Legacy Game, Modular Board, Once-Per-Game Abilities, Scenario / Mission / Campaign Game, Simultaneous Action Selection, Solo / Solitaire Game, Storytelling, Variable Player Powers
2nd rowAction Points, Cooperative Game, Hand Management, Legacy Game, Point to Point Movement, Set Collection, Trading, Variable Player Powers
3rd rowHand Management, Income, Loans, Market, Network and Route Building, Score-and-Reset Game, Tech Trees / Tech Tracks, Turn Order: Stat-Based, Variable Set-up
4th rowCard Drafting, Drafting, End Game Bonuses, Hand Management, Hexagon Grid, Income, Set Collection, Solo / Solitaire Game, Take That, Tile Placement, Turn Order: Progressive, Variable Player Powers
5th rowAction Drafting, Area Majority / Influence, Area-Impulse, Dice Rolling, Follow, Grid Movement, Hexagon Grid, Modular Board, Trading, Variable Phase Order, Variable Player Powers, Voting
ValueCountFrequency (%)
dice 5728
 
4.3%
rolling 5672
 
4.2%
5185
 
3.9%
movement 4247
 
3.2%
hand 4152
 
3.1%
management 4152
 
3.1%
grid 3954
 
2.9%
game 3728
 
2.8%
and 3675
 
2.7%
player 3223
 
2.4%
Other values (269) 90704
67.5%
2023-06-18T14:11:59.121178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
114077
 
11.7%
e 90961
 
9.3%
n 72419
 
7.4%
i 68728
 
7.0%
a 67340
 
6.9%
o 62130
 
6.4%
t 52443
 
5.4%
l 50080
 
5.1%
r 40395
 
4.1%
, 38055
 
3.9%
Other values (46) 319187
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 685744
70.3%
Uppercase Letter 127354
 
13.1%
Space Separator 114077
 
11.7%
Other Punctuation 44583
 
4.6%
Dash Punctuation 4057
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 90961
13.3%
n 72419
10.6%
i 68728
10.0%
a 67340
9.8%
o 62130
9.1%
t 52443
7.6%
l 50080
 
7.3%
r 40395
 
5.9%
d 27444
 
4.0%
c 25105
 
3.7%
Other values (15) 128699
18.8%
Uppercase Letter
ValueCountFrequency (%)
P 17542
13.8%
M 14580
11.4%
S 12947
10.2%
D 12028
9.4%
R 10056
7.9%
C 9208
7.2%
B 8037
 
6.3%
G 7726
 
6.1%
A 6996
 
5.5%
H 6942
 
5.5%
Other values (15) 21292
16.7%
Other Punctuation
ValueCountFrequency (%)
, 38055
85.4%
/ 6282
 
14.1%
: 233
 
0.5%
' 13
 
< 0.1%
Space Separator
ValueCountFrequency (%)
114077
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4057
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 813098
83.3%
Common 162717
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 90961
 
11.2%
n 72419
 
8.9%
i 68728
 
8.5%
a 67340
 
8.3%
o 62130
 
7.6%
t 52443
 
6.4%
l 50080
 
6.2%
r 40395
 
5.0%
d 27444
 
3.4%
c 25105
 
3.1%
Other values (40) 256053
31.5%
Common
ValueCountFrequency (%)
114077
70.1%
, 38055
 
23.4%
/ 6282
 
3.9%
- 4057
 
2.5%
: 233
 
0.1%
' 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 975815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
114077
 
11.7%
e 90961
 
9.3%
n 72419
 
7.4%
i 68728
 
7.0%
a 67340
 
6.9%
o 62130
 
6.4%
t 52443
 
5.4%
l 50080
 
5.1%
r 40395
 
4.1%
, 38055
 
3.9%
Other values (46) 319187
32.7%

Domains
Categorical

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
Not Defined
10159 
Wargames
3029 
Strategy Games
1455 
Family Games
1340 
Abstract Games
 
869
Other values (35)
3491 

Length

Max length46
Median length11
Mean length12.546527
Min length8

Characters and Unicode

Total characters255234
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowStrategy Games, Thematic Games
2nd rowStrategy Games, Thematic Games
3rd rowStrategy Games
4th rowStrategy Games
5th rowStrategy Games, Thematic Games

Common Values

ValueCountFrequency (%)
Not Defined 10159
49.9%
Wargames 3029
 
14.9%
Strategy Games 1455
 
7.2%
Family Games 1340
 
6.6%
Abstract Games 869
 
4.3%
Children's Games 708
 
3.5%
Thematic Games 647
 
3.2%
Party Games 409
 
2.0%
Family Games, Strategy Games 354
 
1.7%
Customizable Games 235
 
1.2%
Other values (30) 1138
 
5.6%

Length

2023-06-18T14:11:59.217692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
not 10159
25.2%
defined 10159
25.2%
games 8373
20.7%
wargames 3316
 
8.2%
strategy 2205
 
5.5%
family 2173
 
5.4%
thematic 1174
 
2.9%
abstract 1070
 
2.6%
children's 849
 
2.1%
party 605
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 36532
14.3%
a 22529
 
8.8%
20037
 
7.9%
t 18785
 
7.4%
m 15333
 
6.0%
i 14652
 
5.7%
s 13905
 
5.4%
d 11008
 
4.3%
n 11008
 
4.3%
o 10456
 
4.1%
Other values (22) 80989
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 192463
75.4%
Uppercase Letter 40380
 
15.8%
Space Separator 20037
 
7.9%
Other Punctuation 2354
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 36532
19.0%
a 22529
11.7%
t 18785
9.8%
m 15333
8.0%
i 14652
7.6%
s 13905
 
7.2%
d 11008
 
5.7%
n 11008
 
5.7%
o 10456
 
5.4%
f 10159
 
5.3%
Other values (9) 28096
14.6%
Uppercase Letter
ValueCountFrequency (%)
N 10159
25.2%
D 10159
25.2%
G 8373
20.7%
W 3316
 
8.2%
S 2205
 
5.5%
F 2173
 
5.4%
T 1174
 
2.9%
C 1146
 
2.8%
A 1070
 
2.6%
P 605
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 1505
63.9%
' 849
36.1%
Space Separator
ValueCountFrequency (%)
20037
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 232843
91.2%
Common 22391
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 36532
15.7%
a 22529
 
9.7%
t 18785
 
8.1%
m 15333
 
6.6%
i 14652
 
6.3%
s 13905
 
6.0%
d 11008
 
4.7%
n 11008
 
4.7%
o 10456
 
4.5%
N 10159
 
4.4%
Other values (19) 68476
29.4%
Common
ValueCountFrequency (%)
20037
89.5%
, 1505
 
6.7%
' 849
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 255234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 36532
14.3%
a 22529
 
8.8%
20037
 
7.9%
t 18785
 
7.4%
m 15333
 
6.0%
i 14652
 
5.7%
s 13905
 
5.4%
d 11008
 
4.3%
n 11008
 
4.3%
o 10456
 
4.1%
Other values (22) 80989
31.7%

Mech Not Defined
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
18745 
1
 
1598

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18745
92.1%
1 1598
 
7.9%

Length

2023-06-18T14:11:59.286211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:11:59.353794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 18745
92.1%
1 1598
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0 18745
92.1%
1 1598
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18745
92.1%
1 1598
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18745
92.1%
1 1598
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18745
92.1%
1 1598
 
7.9%

Mech_Acting
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
18730 
1
 
1613

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 18730
92.1%
1 1613
 
7.9%

Length

2023-06-18T14:11:59.407303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:11:59.469819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 18730
92.1%
1 1613
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0 18730
92.1%
1 1613
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18730
92.1%
1 1613
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18730
92.1%
1 1613
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18730
92.1%
1 1613
 
7.9%

Mech_Action
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
15470 
1
4873 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 15470
76.0%
1 4873
 
24.0%

Length

2023-06-18T14:11:59.521819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:11:59.585341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 15470
76.0%
1 4873
 
24.0%

Most occurring characters

ValueCountFrequency (%)
0 15470
76.0%
1 4873
 
24.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15470
76.0%
1 4873
 
24.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15470
76.0%
1 4873
 
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15470
76.0%
1 4873
 
24.0%

Mech_tokens
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
20314 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20314
99.9%
1 29
 
0.1%

Length

2023-06-18T14:11:59.639335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:11:59.701244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 20314
99.9%
1 29
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 20314
99.9%
1 29
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20314
99.9%
1 29
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20314
99.9%
1 29
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20314
99.9%
1 29
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
18454 
1
1889 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18454
90.7%
1 1889
 
9.3%

Length

2023-06-18T14:11:59.754248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:11:59.815760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 18454
90.7%
1 1889
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 18454
90.7%
1 1889
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18454
90.7%
1 1889
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18454
90.7%
1 1889
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18454
90.7%
1 1889
 
9.3%

Mech_roll_thng
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
13543 
1
6800 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 13543
66.6%
1 6800
33.4%

Length

2023-06-18T14:11:59.870273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:11:59.933273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13543
66.6%
1 6800
33.4%

Most occurring characters

ValueCountFrequency (%)
0 13543
66.6%
1 6800
33.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13543
66.6%
1 6800
33.4%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13543
66.6%
1 6800
33.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13543
66.6%
1 6800
33.4%

Mech_cards
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
12723 
1
7620 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 12723
62.5%
1 7620
37.5%

Length

2023-06-18T14:11:59.987787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:00.054793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 12723
62.5%
1 7620
37.5%

Most occurring characters

ValueCountFrequency (%)
0 12723
62.5%
1 7620
37.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12723
62.5%
1 7620
37.5%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12723
62.5%
1 7620
37.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12723
62.5%
1 7620
37.5%

Mech_role_camp
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
14970 
1
5373 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 14970
73.6%
1 5373
 
26.4%

Length

2023-06-18T14:12:00.113300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:00.175817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 14970
73.6%
1 5373
 
26.4%

Most occurring characters

ValueCountFrequency (%)
0 14970
73.6%
1 5373
 
26.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14970
73.6%
1 5373
 
26.4%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14970
73.6%
1 5373
 
26.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14970
73.6%
1 5373
 
26.4%

Mech_board
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
11975 
1
8368 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 11975
58.9%
1 8368
41.1%

Length

2023-06-18T14:12:00.234863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:00.297376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 11975
58.9%
1 8368
41.1%

Most occurring characters

ValueCountFrequency (%)
0 11975
58.9%
1 8368
41.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11975
58.9%
1 8368
41.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11975
58.9%
1 8368
41.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11975
58.9%
1 8368
41.1%

Mech_money
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
17958 
1
2385 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 17958
88.3%
1 2385
 
11.7%

Length

2023-06-18T14:12:00.356378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:00.422892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 17958
88.3%
1 2385
 
11.7%

Most occurring characters

ValueCountFrequency (%)
0 17958
88.3%
1 2385
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17958
88.3%
1 2385
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17958
88.3%
1 2385
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17958
88.3%
1 2385
 
11.7%

Mech_score
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
19572 
1
 
771

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19572
96.2%
1 771
 
3.8%

Length

2023-06-18T14:12:00.481406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:00.545406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 19572
96.2%
1 771
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 19572
96.2%
1 771
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19572
96.2%
1 771
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19572
96.2%
1 771
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19572
96.2%
1 771
 
3.8%

Mech_turnbased
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
19080 
1
 
1263

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 19080
93.8%
1 1263
 
6.2%

Length

2023-06-18T14:12:00.601921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:00.665431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 19080
93.8%
1 1263
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 19080
93.8%
1 1263
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19080
93.8%
1 1263
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19080
93.8%
1 1263
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19080
93.8%
1 1263
 
6.2%

Mech_team
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
18032 
1
2311 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18032
88.6%
1 2311
 
11.4%

Length

2023-06-18T14:12:00.721432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:00.785949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 18032
88.6%
1 2311
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0 18032
88.6%
1 2311
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18032
88.6%
1 2311
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18032
88.6%
1 2311
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18032
88.6%
1 2311
 
11.4%

Mech_skill
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
19077 
1
 
1266

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19077
93.8%
1 1266
 
6.2%

Length

2023-06-18T14:12:00.846948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:00.910466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 19077
93.8%
1 1266
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 19077
93.8%
1 1266
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19077
93.8%
1 1266
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19077
93.8%
1 1266
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19077
93.8%
1 1266
 
6.2%

Mech_solo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
19641 
1
 
702

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 19641
96.5%
1 702
 
3.5%

Length

2023-06-18T14:12:00.966980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:01.030981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 19641
96.5%
1 702
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 19641
96.5%
1 702
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19641
96.5%
1 702
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19641
96.5%
1 702
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19641
96.5%
1 702
 
3.5%

Abstract
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
19273 
1
 
1070

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19273
94.7%
1 1070
 
5.3%

Length

2023-06-18T14:12:01.084497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:01.147496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 19273
94.7%
1 1070
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 19273
94.7%
1 1070
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19273
94.7%
1 1070
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19273
94.7%
1 1070
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19273
94.7%
1 1070
 
5.3%

Children
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
19494 
1
 
849

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19494
95.8%
1 849
 
4.2%

Length

2023-06-18T14:12:01.201008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:01.262534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 19494
95.8%
1 849
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 19494
95.8%
1 849
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19494
95.8%
1 849
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19494
95.8%
1 849
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19494
95.8%
1 849
 
4.2%

Customizable
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
20046 
1
 
297

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20046
98.5%
1 297
 
1.5%

Length

2023-06-18T14:12:01.315048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:01.377560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 20046
98.5%
1 297
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 20046
98.5%
1 297
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20046
98.5%
1 297
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20046
98.5%
1 297
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20046
98.5%
1 297
 
1.5%

Family
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
18170 
1
2173 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18170
89.3%
1 2173
 
10.7%

Length

2023-06-18T14:12:01.432560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:01.497988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 18170
89.3%
1 2173
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 18170
89.3%
1 2173
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18170
89.3%
1 2173
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18170
89.3%
1 2173
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18170
89.3%
1 2173
 
10.7%

Party
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
19738 
1
 
605

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19738
97.0%
1 605
 
3.0%

Length

2023-06-18T14:12:01.555995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:01.619503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 19738
97.0%
1 605
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 19738
97.0%
1 605
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19738
97.0%
1 605
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19738
97.0%
1 605
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19738
97.0%
1 605
 
3.0%

Strategy
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
18138 
1
2205 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 18138
89.2%
1 2205
 
10.8%

Length

2023-06-18T14:12:01.675019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:01.740564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 18138
89.2%
1 2205
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 18138
89.2%
1 2205
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18138
89.2%
1 2205
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18138
89.2%
1 2205
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18138
89.2%
1 2205
 
10.8%

Thematic
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
19169 
1
 
1174

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 19169
94.2%
1 1174
 
5.8%

Length

2023-06-18T14:12:01.795080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:01.858094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 19169
94.2%
1 1174
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 19169
94.2%
1 1174
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19169
94.2%
1 1174
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19169
94.2%
1 1174
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19169
94.2%
1 1174
 
5.8%

Wargames
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
17027 
1
3316 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 17027
83.7%
1 3316
 
16.3%

Length

2023-06-18T14:12:01.910599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:01.973115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 17027
83.7%
1 3316
 
16.3%

Most occurring characters

ValueCountFrequency (%)
0 17027
83.7%
1 3316
 
16.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17027
83.7%
1 3316
 
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17027
83.7%
1 3316
 
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17027
83.7%
1 3316
 
16.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
0
10184 
1
10159 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10184
50.1%
1 10159
49.9%

Length

2023-06-18T14:12:02.028116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T14:12:02.090635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 10184
50.1%
1 10159
49.9%

Most occurring characters

ValueCountFrequency (%)
0 10184
50.1%
1 10159
49.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10184
50.1%
1 10159
49.9%

Most occurring scripts

ValueCountFrequency (%)
Common 20343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10184
50.1%
1 10159
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10184
50.1%
1 10159
49.9%

Interactions

2023-06-18T14:11:54.463114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:45.573514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:46.427193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:47.417916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:48.248554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:49.073508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:50.071147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:50.905003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:51.735125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:52.548752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:53.444395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:54.546114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:45.656520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:46.621230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:47.491431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:48.323064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:49.153516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:50.146150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:50.980517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:51.808637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:52.630262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:53.520911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:54.635632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:45.739030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:46.705742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:47.569952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:48.402580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:49.236024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:50.225659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:51.061027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:51.887157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:52.715780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:53.601428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:54.715270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:45.813548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:46.783259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:47.640952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:48.474098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:49.311516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:50.299178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:51.133031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:51.959160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:52.793300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:53.673943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:54.795790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:45.889063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:46.859767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:47.712468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:48.547102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:49.387028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:50.370697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:51.206550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:52.029672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:52.870820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:53.746948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:54.880301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:45.967575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:46.939771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:47.788981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:48.623614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:49.462539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:50.446701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:51.283064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:52.103187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:52.951823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:53.823459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:54.960308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:46.041577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:47.017288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:47.861492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:48.695128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:49.537542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:50.518208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:51.356067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:52.175698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:53.030333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:53.895975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:55.040788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:46.116086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:47.095806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:47.934493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:48.768648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:49.613056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:50.592722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:51.429576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:52.247703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:53.108848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:54.155521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:55.119306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:46.189600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:47.170887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:48.004006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:48.839649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:49.686572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:50.663239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:51.501092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:52.317724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:53.186365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:54.228031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:55.206819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:46.270679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:47.253891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:48.085029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:48.919165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:49.768088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:50.743238image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:51.581611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:52.396235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:53.269877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:54.306548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:55.287332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:46.345685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:47.332402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:48.162553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:48.993191image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:49.988627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:50.819490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:51.655613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:52.467748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:53.353883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-18T14:11:54.380062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-06-18T14:12:02.169150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
IDYear PublishedMin PlayersMax PlayersPlay TimeMin AgeUsers RatedRating AverageBGG RankComplexity AverageOwned UsersDomainsMech Not DefinedMech_ActingMech_ActionMech_tokensMech_construcc_farmMech_roll_thngMech_cardsMech_role_campMech_boardMech_moneyMech_scoreMech_turnbasedMech_teamMech_skillMech_soloAbstractChildrenCustomizableFamilyPartyStrategyThematicWargamesDomain_Not Defined
ID1.0000.948-0.1400.045-0.1010.079-0.0230.397-0.225-0.060-0.0130.1440.0920.1020.1820.0000.0570.0820.2360.0830.0780.0130.0700.0470.1930.0500.1600.1600.1630.0660.0780.0420.0870.0600.2610.346
Year Published0.9481.000-0.1320.061-0.0660.1180.0520.423-0.285-0.0220.0630.1540.0100.0000.0000.0200.0140.0230.0130.0220.0240.0380.0000.0220.0170.0000.0000.1300.0060.0000.0150.0350.0000.0000.0130.024
Min Players-0.140-0.1321.0000.316-0.094-0.014-0.014-0.2080.115-0.192-0.0560.1200.0530.3050.0600.0000.0490.1740.0610.1670.2020.1810.0830.0600.2910.0430.3970.0950.0740.0570.0590.2350.0650.1120.1690.117
Max Players0.0450.0610.3161.000-0.090-0.0120.074-0.1880.083-0.2980.0070.0000.0240.0230.0090.0000.0000.0050.0100.0180.0130.0000.0000.0000.0000.0070.0290.0000.0000.0000.0000.0220.0000.0000.0000.000
Play Time-0.101-0.066-0.094-0.0901.0000.4660.1330.362-0.2660.6690.2100.0000.0000.0000.0000.0000.0000.0160.0080.0220.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.009
Min Age0.0790.118-0.014-0.0120.4661.0000.1650.282-0.2530.4330.2240.2620.1370.1610.1360.0350.0710.1630.1940.3140.2880.0940.0570.0840.1510.1240.1130.1540.5160.0420.2340.0560.2050.1590.4630.226
Users Rated-0.0230.052-0.0140.0740.1330.1651.0000.250-0.7100.1720.9250.0730.0300.0820.0770.0980.0440.0090.0790.0790.0230.0710.0850.0800.0640.0320.1080.0000.0160.0100.0960.0600.1410.0810.0420.123
Rating Average0.3970.423-0.208-0.1880.3620.2820.2501.000-0.7660.5070.2720.1790.1870.0780.1440.0290.0650.1550.1020.3240.2550.0290.0410.0860.1400.0470.2110.0650.2090.0090.1260.0540.2180.0970.2130.208
BGG Rank-0.225-0.2850.1150.083-0.266-0.253-0.710-0.7661.000-0.400-0.6880.2310.1940.0800.1650.0550.1090.0700.1920.2500.2120.1240.0820.0680.1330.0430.1940.0190.1910.0450.2890.1360.4690.2450.1780.441
Complexity Average-0.060-0.022-0.192-0.2980.6690.4330.1720.507-0.4001.0000.2380.2590.1990.0590.0410.0240.0660.1920.1500.4180.4740.1050.0390.1020.0440.1190.1160.1030.2610.0870.2550.2130.3320.1630.4850.408
Owned Users-0.0130.063-0.0560.0070.2100.2240.9250.272-0.6880.2381.0000.0840.0300.0990.0760.0740.0390.0190.0750.0790.0240.0670.0840.0700.0770.0270.1170.0000.0170.0180.1070.0720.1170.0890.0440.122
Domains0.1440.1540.1200.0000.0000.2620.0730.1790.2310.2590.0841.0000.1820.2790.1620.0480.2320.3570.3160.3740.5140.2660.1120.1010.2650.1440.1490.9990.9990.9990.9990.9990.9990.9990.9990.999
Mech Not Defined0.0920.0100.0530.0240.0000.1370.0300.1870.1940.1990.0300.1821.0000.0850.1640.0050.0930.2070.2260.1750.2440.1060.0570.0740.1040.0750.0540.0370.0490.0310.0400.0030.0880.0460.1140.118
Mech_Acting0.1020.0000.3050.0230.0000.1610.0820.0780.0800.0590.0990.2790.0851.0000.0400.0000.0490.0160.0490.0480.0740.0150.0390.0060.2800.0620.0510.0640.0450.0000.0550.1690.0060.1530.0920.033
Mech_Action0.1820.0000.0600.0090.0000.1360.0770.1440.1650.0410.0760.1620.1640.0401.0000.0080.0110.0470.0880.1120.0150.0000.0680.0100.0500.0300.0760.0800.0590.0000.0480.0400.0600.0730.0670.017
Mech_tokens0.0000.0200.0000.0000.0000.0350.0980.0290.0550.0240.0740.0480.0050.0000.0081.0000.0160.0000.0010.0000.0000.0280.0000.0190.0000.0010.0160.0000.0270.0000.0000.0000.0300.0000.0000.028
Mech_construcc_farm0.0570.0140.0490.0000.0000.0710.0440.0650.1090.0660.0390.2320.0930.0490.0110.0161.0000.0750.0170.0410.0860.1010.0060.0420.0380.0180.0150.1260.0010.0300.0590.0420.1300.0270.1310.026
Mech_roll_thng0.0820.0230.1740.0050.0160.1630.0090.1550.0700.1920.0190.3570.2070.0160.0470.0000.0751.0000.1510.2610.1710.0480.0340.0090.0130.0790.0850.1210.0330.0190.0500.0640.0680.1170.3050.136
Mech_cards0.2360.0130.0610.0100.0080.1940.0790.1020.1920.1500.0750.3160.2260.0490.0880.0010.0170.1511.0000.0000.1410.0970.0720.0330.0080.0070.0540.1030.0710.0510.1350.0250.1440.0200.2140.074
Mech_role_camp0.0830.0220.1670.0180.0220.3140.0790.3240.2500.4180.0790.3740.1750.0480.1120.0000.0410.2610.0001.0000.2720.0280.0240.0600.0950.0530.1220.0790.1130.0310.0930.0500.1110.1600.2780.182
Mech_board0.0780.0240.2020.0130.0150.2880.0230.2550.2120.4740.0240.5140.2440.0740.0150.0000.0860.1710.1410.2721.0000.0810.0340.0340.0160.0740.0640.0950.1000.0350.0550.1250.1250.0620.4350.321
Mech_money0.0130.0380.1810.0000.0000.0940.0710.0290.1240.1050.0670.2660.1060.0150.0000.0280.1010.0480.0970.0280.0811.0000.1010.0120.0580.0190.0000.0520.0390.0250.0630.0000.2080.0100.1470.010
Mech_score0.0700.0000.0830.0000.0000.0570.0850.0410.0820.0390.0840.1120.0570.0390.0680.0000.0060.0340.0720.0240.0340.1011.0000.0100.0260.0210.0040.0200.0260.0000.0000.0670.0040.0260.0510.027
Mech_turnbased0.0470.0220.0600.0000.0000.0840.0800.0860.0680.1020.0700.1010.0740.0060.0100.0190.0420.0090.0330.0600.0340.0120.0101.0000.0000.0240.0430.0000.0410.0120.0170.0000.0420.0090.0600.066
Mech_team0.1930.0170.2910.0000.0000.1510.0640.1400.1330.0440.0770.2650.1040.2800.0500.0000.0380.0130.0080.0950.0160.0580.0260.0001.0000.0600.1290.0500.0170.0220.0300.0760.0170.1890.1130.043
Mech_skill0.0500.0000.0430.0070.0000.1240.0320.0470.0430.1190.0270.1440.0750.0620.0300.0010.0180.0790.0070.0530.0740.0190.0210.0240.0601.0000.0040.0070.0880.0100.0120.0570.0210.0000.0970.034
Mech_solo0.1600.0000.3970.0290.0000.1130.1080.2110.1940.1160.1170.1490.0540.0510.0760.0160.0150.0850.0540.1220.0640.0000.0040.0430.1290.0041.0000.0340.0350.0080.0000.0230.0700.1040.0460.072
Abstract0.1600.1300.0950.0000.0000.1540.0000.0650.0190.1030.0000.9990.0370.0640.0800.0000.1260.1210.1030.0790.0950.0520.0200.0000.0500.0070.0341.0000.0210.0230.0000.0350.0520.0560.0950.235
Children0.1630.0060.0740.0000.0000.5160.0160.2090.1910.2610.0170.9990.0490.0450.0590.0270.0010.0330.0710.1130.1000.0390.0260.0410.0170.0880.0350.0211.0000.0230.0100.0230.0720.0510.0880.208
Customizable0.0660.0000.0570.0000.0000.0420.0100.0090.0450.0870.0180.9990.0310.0000.0000.0000.0300.0190.0510.0310.0350.0250.0000.0120.0220.0100.0080.0230.0231.0000.0410.0190.0030.0000.0310.121
Family0.0780.0150.0590.0000.0000.2340.0960.1260.2890.2550.1070.9990.0400.0550.0480.0000.0590.0500.1350.0930.0550.0630.0000.0170.0300.0120.0000.0000.0100.0411.0000.0720.0600.0050.1500.345
Party0.0420.0350.2350.0220.0000.0560.0600.0540.1360.2130.0720.9990.0030.1690.0400.0000.0420.0640.0250.0500.1250.0000.0670.0000.0760.0570.0230.0350.0230.0190.0721.0000.0540.0000.0760.174
Strategy0.0870.0000.0650.0000.0000.2050.1410.2180.4690.3320.1170.9990.0880.0060.0600.0300.1300.0680.1440.1110.1250.2080.0040.0420.0170.0210.0700.0520.0720.0030.0600.0541.0000.0650.1090.348
Thematic0.0600.0000.1120.0000.0000.1590.0810.0970.2450.1630.0890.9990.0460.1530.0730.0000.0270.1170.0200.1600.0620.0100.0260.0090.1890.0000.1040.0560.0510.0000.0050.0000.0651.0000.0250.247
Wargames0.2610.0130.1690.0000.0390.4630.0420.2130.1780.4850.0440.9990.1140.0920.0670.0000.1310.3050.2140.2780.4350.1470.0510.0600.1130.0970.0460.0950.0880.0310.1500.0760.1090.0251.0000.441
Domain_Not Defined0.3460.0240.1170.0000.0090.2260.1230.2080.4410.4080.1220.9990.1180.0330.0170.0280.0260.1360.0740.1820.3210.0100.0270.0660.0430.0340.0720.2350.2080.1210.3450.1740.3480.2470.4411.000

Missing values

2023-06-18T14:11:55.440847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-18T14:11:55.929415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDNameYear PublishedMin PlayersMax PlayersPlay TimeMin AgeUsers RatedRating AverageBGG RankComplexity AverageOwned UsersMechanicsDomainsMech Not DefinedMech_ActingMech_ActionMech_tokensMech_construcc_farmMech_roll_thngMech_cardsMech_role_campMech_boardMech_moneyMech_scoreMech_turnbasedMech_teamMech_skillMech_soloAbstractChildrenCustomizableFamilyPartyStrategyThematicWargamesDomain_Not Defined
0174430.0Gloomhaven20171412014420558.7913.8668323Action Queue, Action Retrieval, Campaign / Battle Card Driven, Card Play Conflict Resolution, Communication Limits, Cooperative Game, Deck Construction, Deck Bag and Pool Building, Grid Movement, Hand Management, Hexagon Grid, Legacy Game, Modular Board, Once-Per-Game Abilities, Scenario / Mission / Campaign Game, Simultaneous Action Selection, Solo / Solitaire Game, Storytelling, Variable Player PowersStrategy Games, Thematic Games011000111000111000001100
1161936.0Pandemic Legacy: Season 12015246013416438.6122.8465294Action Points, Cooperative Game, Hand Management, Legacy Game, Point to Point Movement, Set Collection, Trading, Variable Player PowersStrategy Games, Thematic Games001000111100100000001100
2224517.0Brass: Birmingham20182412014192178.6633.9128785Hand Management, Income, Loans, Market, Network and Route Building, Score-and-Reset Game, Tech Trees / Tech Tracks, Turn Order: Stat-Based, Variable Set-upStrategy Games000011100111000000001000
3167791.0Terraforming Mars20161512012648648.4343.2487099Card Drafting, Drafting, End Game Bonuses, Hand Management, Hexagon Grid, Income, Set Collection, Solo / Solitaire Game, Take That, Tile Placement, Turn Order: Progressive, Variable Player PowersStrategy Games001000111101001000001000
4233078.0Twilight Imperium: Fourth Edition20173648014134688.7054.2216831Action Drafting, Area Majority / Influence, Area-Impulse, Dice Rolling, Follow, Grid Movement, Hexagon Grid, Modular Board, Trading, Variable Phase Order, Variable Player Powers, VotingStrategy Games, Thematic Games011001011101000000001100
5291457.0Gloomhaven: Jaws of the Lion2020141201483928.8763.5521609Action Queue, Campaign / Battle Card Driven, Communication Limits, Cooperative Game, Critical Hits and Failures, Deck Construction, Grid Movement, Hand Management, Hexagon Grid, Legacy Game, Line of Sight, Once-Per-Game Abilities, Scenario / Mission / Campaign Game, Simultaneous Action Selection, Solo / Solitaire Game, Variable Player PowersStrategy Games, Thematic Games011000111000111000001100
6182028.0Through the Ages: A New Story of Civilization20152412014230618.4374.4126985Action Points, Auction/Bidding, Auction: Dutch, Card Drafting, Events, Income, Take ThatStrategy Games001000110100000000001000
7220308.0Gaia Project20171415012163528.4984.3520312End Game Bonuses, Hexagon Grid, Income, Modular Board, Network and Route Building, Solo / Solitaire Game, Tech Trees / Tech Tracks, Turn Order: Pass Order, Variable Player Powers, Variable Set-up, Victory Points as a ResourceStrategy Games000011011111001000001000
8187645.0Star Wars: Rebellion20162424014230818.4293.7134849Area Majority / Influence, Area Movement, Area-Impulse, Delayed Purchase, Dice Rolling, Hand Management, Team-Based Game, Variable Player PowersThematic Games000001111100100000000100
912333.0Twilight Struggle20052218013408148.29103.5956219Action/Event, Advantage Token, Area Majority / Influence, Campaign / Battle Card Driven, Dice Rolling, Hand Management, Simulation, Simultaneous Action Selection, Sudden Death Ending, Tug of WarStrategy Games, Wargames001101111000100000001010
IDNameYear PublishedMin PlayersMax PlayersPlay TimeMin AgeUsers RatedRating AverageBGG RankComplexity AverageOwned UsersMechanicsDomainsMech Not DefinedMech_ActingMech_ActionMech_tokensMech_construcc_farmMech_roll_thngMech_cardsMech_role_campMech_boardMech_moneyMech_scoreMech_turnbasedMech_teamMech_skillMech_soloAbstractChildrenCustomizableFamilyPartyStrategyThematicWargamesDomain_Not Defined
203333737.0Operation19651610636174.10203351.116096SimulationChildren's Games000000010000000010000000
203343522.0LCR198331220518353.42203361.053441Dice RollingChildren's Games, Party Games000001000000000010010000
203351406.0Monopoly1933281808289994.39203371.6540255Auction/Bidding, Income, Loans, Lose a Turn, Player Elimination, Roll / Spin and Move, Set Collection, Stock Holding, Track Movement, TradingFamily Games001001100111000000100000
203362921.0The Game of Life196026608106584.30203381.1816692Roll / Spin and Move, SimulationFamily Games000001010000000000100000
203371410.0Trouble19652445432553.79203391.054962Roll / Spin and MoveChildren's Games000001000000000010000000
2033816398.0War19862230413402.28203401.00427Not DefinedChildren's Games100000000000000010000000
203397316.0Bingo153029960521542.85203411.051533Betting and Bluffing, Bingo, Pattern RecognitionParty Games000000000101000000010000
203405048.0Candy Land19492430340063.18203421.085788Roll / Spin and MoveChildren's Games000001000000000010000000
203415432.0Chutes and Ladders19862630337832.86203431.024400Dice Rolling, Grid Movement, Race, Roll / Spin and Move, Square GridChildren's Games001001001000000010000000
2034211901.0Tic-Tac-Toe1986221432752.68203441.161374Paper-and-Pencil, Pattern BuildingAbstract Games, Children's Games001010000000000110000000